
#Importing Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns


#turning off warnings for final notebook
import warnings
warnings.filterwarnings('ignore')


pd.set_option('display.max_columns', None)
sns.set_context('notebook')
sns.set_style('whitegrid')
sns.set_palette('Blues_r')


#importing the file
df = pd.read_csv('marketing_data.csv')


df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 28 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   ID                   2240 non-null   int64 
 1   Year_Birth           2240 non-null   int64 
 2   Education            2240 non-null   object
 3   Marital_Status       2240 non-null   object
 4    Income              2216 non-null   object
 5   Kidhome              2240 non-null   int64 
 6   Teenhome             2240 non-null   int64 
 7   Dt_Customer          2240 non-null   object
 8   Recency              2240 non-null   int64 
 9   MntWines             2240 non-null   int64 
 10  MntFruits            2240 non-null   int64 
 11  MntMeatProducts      2240 non-null   int64 
 12  MntFishProducts      2240 non-null   int64 
 13  MntSweetProducts     2240 non-null   int64 
 14  MntGoldProds         2240 non-null   int64 
 15  NumDealsPurchases    2240 non-null   int64 
 16  NumWebPurchases      2240 non-null   int64 
 17  NumCatalogPurchases  2240 non-null   int64 
 18  NumStorePurchases    2240 non-null   int64 
 19  NumWebVisitsMonth    2240 non-null   int64 
 20  AcceptedCmp3         2240 non-null   int64 
 21  AcceptedCmp4         2240 non-null   int64 
 22  AcceptedCmp5         2240 non-null   int64 
 23  AcceptedCmp1         2240 non-null   int64 
 24  AcceptedCmp2         2240 non-null   int64 
 25  Response             2240 non-null   int64 
 26  Complain             2240 non-null   int64 
 27  Country              2240 non-null   object
dtypes: int64(23), object(5)
memory usage: 490.1+ KB


df.columns

Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', ' Income ',
       'Kidhome', 'Teenhome', 'Dt_Customer', 'Recency', 'MntWines',
       'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',
       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',
       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',
       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',
       'AcceptedCmp2', 'Response', 'Complain', 'Country'],
      dtype='object')


df.isnull().sum()

ID                      0
Year_Birth              0
Education               0
Marital_Status          0
 Income                24
Kidhome                 0
Teenhome                0
Dt_Customer             0
Recency                 0
MntWines                0
MntFruits               0
MntMeatProducts         0
MntFishProducts         0
MntSweetProducts        0
MntGoldProds            0
NumDealsPurchases       0
NumWebPurchases         0
NumCatalogPurchases     0
NumStorePurchases       0
NumWebVisitsMonth       0
AcceptedCmp3            0
AcceptedCmp4            0
AcceptedCmp5            0
AcceptedCmp1            0
AcceptedCmp2            0
Response                0
Complain                0
Country                 0
dtype: int64


#lets work with the null values of Income
#We need to plot a feature to identify the best strategy for imputation


plt.figure(figsize=(8,4))
sns.distplot(df['Income'], kde=False, hist=True)
plt.title('Income distribution', size=16)
plt.ylabel('count');


df['Income'].plot(kind='box', figsize=(3,4), patch_artist=True)

<AxesSubplot:>


#Finding from the above graph:
#most income is distributed between $0-$100,000 with a few outliers


#adding median values to the null values to remove the outliers 
df['Income'] = df['Income'].fillna(df['Income']).median()

#checking null values incase
df['Income'].isnull().sum()

0


#cleaning the column space containing whitespaces 
df.columns = df.columns.str.replace(' ', '')


df.columns    
#we are good to go.

Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',
       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',
       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',
       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',
       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',
       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',
       'AcceptedCmp2', 'Response', 'Complain', 'Country'],
      dtype='object')


df.head()


df['Income'] = df['Income'].str.replace(' ', '')

#removing $ and , from the data and transforming into numerical column
df['Income']=df['Income'].str.replace('$','')
df['Income']=df['Income'].str.replace(',','').astype('float')


#new dataset
df.head()


#to check for the changes in the data type
print(df['Income'].dtypes)

float64


#Identifying features containing outliers
# select columns to plot
df_to_plot = df.drop(columns=['ID', 'AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Response', 'Complain']).select_dtypes(include=np.number)
# subplots
df_to_plot.plot(subplots=True, layout=(4,4), kind='box', figsize=(12,14), patch_artist=True)
plt.subplots_adjust(wspace=0.5);


df= df[df['Year_Birth']>1900].reset_index(drop=True)

#checking for the same:
plt.figure(figsize=(3,4))
df['Year_Birth'].plot(kind='box', patch_artist=True)

<AxesSubplot:>


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2237 entries, 0 to 2236
Data columns (total 28 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2237 non-null   int64  
 1   Year_Birth           2237 non-null   int64  
 2   Education            2237 non-null   object 
 3   Marital_Status       2237 non-null   object 
 4   Income               2237 non-null   float64
 5   Kidhome              2237 non-null   int64  
 6   Teenhome             2237 non-null   int64  
 7   Dt_Customer          2237 non-null   object 
 8   Recency              2237 non-null   int64  
 9   MntWines             2237 non-null   int64  
 10  MntFruits            2237 non-null   int64  
 11  MntMeatProducts      2237 non-null   int64  
 12  MntFishProducts      2237 non-null   int64  
 13  MntSweetProducts     2237 non-null   int64  
 14  MntGoldProds         2237 non-null   int64  
 15  NumDealsPurchases    2237 non-null   int64  
 16  NumWebPurchases      2237 non-null   int64  
 17  NumCatalogPurchases  2237 non-null   int64  
 18  NumStorePurchases    2237 non-null   int64  
 19  NumWebVisitsMonth    2237 non-null   int64  
 20  AcceptedCmp3         2237 non-null   int64  
 21  AcceptedCmp4         2237 non-null   int64  
 22  AcceptedCmp5         2237 non-null   int64  
 23  AcceptedCmp1         2237 non-null   int64  
 24  AcceptedCmp2         2237 non-null   int64  
 25  Response             2237 non-null   int64  
 26  Complain             2237 non-null   int64  
 27  Country              2237 non-null   object 
dtypes: float64(1), int64(23), object(4)
memory usage: 489.5+ KB


df['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'])

#to check:
print(df['Dt_Customer'].dtypes)

datetime64[ns]


list(df.columns)

['ID',
 'Year_Birth',
 'Education',
 'Marital_Status',
 'Income',
 'Kidhome',
 'Teenhome',
 'Dt_Customer',
 'Recency',
 'MntWines',
 'MntFruits',
 'MntMeatProducts',
 'MntFishProducts',
 'MntSweetProducts',
 'MntGoldProds',
 'NumDealsPurchases',
 'NumWebPurchases',
 'NumCatalogPurchases',
 'NumStorePurchases',
 'NumWebVisitsMonth',
 'AcceptedCmp3',
 'AcceptedCmp4',
 'AcceptedCmp5',
 'AcceptedCmp1',
 'AcceptedCmp2',
 'Response',
 'Complain',
 'Country']


# Dependents
df['Dependents']= df['Kidhome']+df['Teenhome']

# Year becoming a Customer
df['Year_Customer'] = pd.DatetimeIndex(df['Dt_Customer']).year

# Total Amount Spent
mnt_cols = [col for col in df.columns if 'Mnt' in col]
df['TotalMnt'] = df[mnt_cols].sum(axis=1)

# Total Purchases
purchases_cols = [col for col in df.columns if 'Purchases' in col]
df['TotalPurchases'] = df[purchases_cols].sum(axis=1)

# Total Campaigns Accepted
campaigns_cols = [col for col in df.columns if 'Cmp' in col] + ['Response'] # 'Response' is for the latest campaign
df['TotalCampaignsAcc'] = df[campaigns_cols].sum(axis=1)

# view new features, by customer ID
df[['ID', 'Dependents', 'Year_Customer', 'TotalMnt', 'TotalPurchases', 'TotalCampaignsAcc']].head()


# calculate correlation matrix
## using non-parametric test of correlation (kendall), since some features are binary
corrs = df.drop(columns='ID').select_dtypes(include=np.number).corr(method = 'kendall')

# plot clustered heatmap of correlations
sns.heatmap(df.corr(), cmap='coolwarm', center=0)

<AxesSubplot:>


#Plot illustrating negative effect of having dependents (kids & teens) on spending:

plt.figure(figsize=(4,4))
sns.boxplot(x='Dependents', y='TotalMnt', data=df);


#Plot illustrating positive effect of having dependents (kids & teens) on number of deals purchased:

plt.figure(figsize=(4,4))
sns.boxplot(x='Dependents', y='NumDealsPurchases', data=df);


df.head()


#Using boxplot to plot correlation between TotalCampaignsAcc and Dependents

plt.figure(figsize=(5.5,4))
sns.boxplot(x='TotalCampaignsAcc', y='Dependents', data=df);


Comparing two lmplots: 
    One between Number of Web Visits per month and Number of Web Purchases
    Second between Number of Web Visits per month and Number of Discount Purchases


sns.lmplot(x='NumWebVisitsMonth', y='NumWebPurchases', data=df)


sns.lmplot(x='NumWebVisitsMonth', y='NumDealsPurchases', data=df)


df.head()


#Plotting the target variable:

plt.figure(figsize=(8,3))
sns.distplot(df['NumStorePurchases'], kde=False , hist=True, bins=12)
plt.title('NumStorePurchases Distribution', size=16)
plt.ylabel('Counts');


#dropping uninformative columns
df.drop(columns=['ID', 'Dt_Customer'], inplace=True)


#one-hot encoding
from sklearn.preprocessing import OneHotEncoder


#get categorical features and review number of unique values
cat = df.select_dtypes(exclude = np.number)
print('Number of unique values per categorical feature: \n', cat.nunique())

Number of unique values per categorical feature: 
 Education         5
Marital_Status    8
Country           8
dtype: int64


enc = OneHotEncoder(sparse =False).fit(cat)
cat_encoded = pd.DataFrame(enc.transform(cat))
cat_encoded.columns = enc.get_feature_names(cat.columns)


#merge with numeric data
num=df.drop(columns=cat.columns)
df2 = pd.concat([cat_encoded, num], axis=1)
df2.head()


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error


#performing train_test_split and isolating X and y variables
X= df2.drop(columns='NumStorePurchases')
y= df2['NumStorePurchases']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)


#implementing LinearRegression model
model =LinearRegression()
model.fit(X_test, y_test)

#predictions
predictions = model.predict(X_test)


#evaluate the model using RSME
print('Linear Regression using RSME:'), np.sqrt(mean_squared_error(y_test, predictions))

Linear Regression using RSME:

(None, 2.052254475267509e-13)


#median value of target variable
print('The median of target variable:', y.median())

The median of target variable: 5.0


import eli5
from eli5.sklearn import PermutationImportance


perm = PermutationImportance(model, random_state=1).fit(X_test, y_test)
eli5.show_weights(perm, feature_names = X_test.columns.tolist(), top=5)


import shap 

# calculate shap values 
ex = shap.Explainer(model, X_train)
shap_values = ex(X_test)

# plot
plt.title('SHAP summary for NumStorePurchases', size=16)
shap.plots.beeswarm(shap_values, max_display=5);


#Plotting total number of purchases by country:

plt.figure(figsize=(5,4))
df.groupby('Country')['TotalPurchases'].sum().sort_values(ascending=False).plot(kind='bar')
plt.title('Total Number of Purchases by Country', size=16)
plt.ylabel('Number of Purchases')


#Plotting total amount spent by country:

plt.figure(figsize=(5,4))
df.groupby('Country')['TotalMnt'].sum().sort_values(ascending=False).plot(kind='bar')
plt.title('Total Amount Spent by Country', size=16)
plt.ylabel('Amount Spent')

Text(0, 0.5, 'Amount Spent')


sns.lmplot(x='MntGoldProds', y='NumStorePurchases', data= df)

<seaborn.axisgrid.FacetGrid at 0x19a490734c0>


from scipy.stats import kendalltau

kendall_corr = kendalltau(x=df['MntGoldProds'], y=df['NumStorePurchases'])

# print results
print('Kendall correlation (tau): ', kendall_corr.correlation)
print('Kendall p-value: ', kendall_corr.pvalue)

Kendall correlation (tau):  0.3927160395725131
Kendall p-value:  3.55881817905435e-152


# sum the marital status and phd dummy variables - the Married+PhD group will have value of 2
df2['Married_PhD'] = df2['Marital_Status_Married'] + df2['Education_PhD']
df2['Married_PhD'] = df2['Married_PhD'].replace({2:'Married-PhD', 1:'Other', 0:'Other'})


# plot MntFishProducts between Married-PhD and others
plt.figure(figsize=(2.5,4))
sns.boxplot(x='Married_PhD', y='MntFishProducts', data=df2, palette="Set2");


# independent t-test p-value
from scipy.stats import ttest_ind
pval = ttest_ind(df2[df2['Married_PhD'] == 'Married-PhD']['MntFishProducts'], df2[df2['Married_PhD'] == 'Other']['MntFishProducts']).pvalue
print("t-test p-value: ", round(pval, 3))

t-test p-value:  0.005


# now drop the married-phD column created above, to include only the original variables in the analysis below
df2.drop(columns='Married_PhD', inplace=True)


plt.figure(figsize=(10,6))
sns.distplot(df['MntFishProducts'], kde=False, hist=True, bins=12)
plt.title('MntFishProducts distribution', size=16)
plt.ylabel('count')

Text(0, 0.5, 'count')


#we will create the Linearregression model now.

X= df2.drop(columns='MntFishProducts')
y= df2['MntFishProducts']

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=1)


model = LinearRegression()


model.fit(X_train, y_train)

LinearRegression()


predict = model.predict(X_test)


print('Linear Regression Model, RMSE:', np.sqrt(mean_squared_error(y_test,predict)))
print('\n')
print('The median value of target variable:', y.median())

Linear Regression Model, RMSE: 8.5839828444711e-13


The median value of target variable: 12.0


perm = PermutationImportance(model, random_state=1).fit(X_test, y_test)
eli5.show_weights(perm, feature_names = X_test.columns.tolist(), top=7)


#We will follow the same technique of Shap values as we have used above:


import shap

# calculate shap values 
ex = shap.Explainer(model, X_train)
shap_values = ex(X_test)

# plot
plt.title('SHAP summary for MntFishProducts', size=16)
shap.plots.beeswarm(shap_values, max_display=7);


# convert country codes to correct nomenclature for choropleth plot
# the dataset doesn't provide information about country codes
#so I'm taking my best guess about the largest nations that make sense given the codes provided

df['Country_code'] = df['Country'].replace({'SP': 'ESP', 'CA': 'CAN', 'US': 'USA', 'SA': 'ZAF', 'ME': 'MEX'})


# success of campaigns by country code
#using melt functions(melt()-Unpivot a DataFrame from wide to long format, optionally leaving identifiers set)

df_cam = df[['Country_code', 'AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Response']].melt(
    id_vars='Country_code', var_name='Campaign', value_name='Accepted (%)')


df_cam.head()


df_cam = pd.DataFrame(df_cam.groupby(['Country_code', 'Campaign'])['Accepted (%)'].mean()*100).reset_index(drop=False)


# rename the campaign variables so they're easier to interpret
df_cam['Campaign'] = df_cam['Campaign'].replace({'AcceptedCmp1': '1',
                                                'AcceptedCmp2': '2',
                                                'AcceptedCmp3': '3',
                                                'AcceptedCmp4': '4',
                                                'AcceptedCmp5': '5',
                                                 'Response': 'Most recent'
                                                })


df_cam['Campaign'].head()

0    1
1    2
2    3
3    4
4    5
Name: Campaign, dtype: object


# choropleth plot
import plotly.express as px

fig = px.choropleth(df_cam, locationmode='ISO-3', color='Accepted (%)', facet_col='Campaign', facet_col_wrap=2,
                    facet_row_spacing=0.05, facet_col_spacing=0.01, width=700,
                    locations='Country_code', projection='natural earth', title='Advertising Campaign Success Rate by Country'
                   )
fig.show()


# calculate logistic regression p-values for campaign acceptance ~ country using generalized linear model
import statsmodels.formula.api as smf
import statsmodels as sm
from scipy import stats


## get the data of interest for Genralised Linear Model
df_cam_wide = df[['Country', 'AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Response']]


df_cam_wide.head()


## to store statistics results
stat_results = []


## perform glm
for col in df_cam_wide.drop(columns='Country').columns:
    this_data = df_cam_wide[['Country', col]]
    
    # define formula
    formula = col+'~Country'
    
    model = smf.glm(formula =formula, data =this_data, family=sm.genmod.families.Binomial())
    result = model.fit()


# get chisquare value for overall model (CampaignAccepted ~ Country) and calculate p-value

chisq = result.pearson_chi2
pval = stats.distributions.chi2.sf(chisq, 7)    #df Model =7 degrees of freedom when we run result.summary()


# append to stat_results
stat_results.append(pval)


# print stat summary for entire model
print(result.summary())

## check results
print("\nChisq p-values: ", stat_results)

                 Generalized Linear Model Regression Results                  
==============================================================================
Dep. Variable:               Response   No. Observations:                 2237
Model:                            GLM   Df Residuals:                     2229
Model Family:                Binomial   Df Model:                            7
Link Function:                  logit   Scale:                          1.0000
Method:                          IRLS   Log-Likelihood:                -937.28
Date:                Sat, 20 Mar 2021   Deviance:                       1874.6
Time:                        23:19:09   Pearson chi2:                 2.24e+03
No. Iterations:                     5                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------
Intercept         -1.7845      0.225     -7.919      0.000      -2.226      -1.343
Country[T.CA]     -0.0160      0.285     -0.056      0.955      -0.575       0.543
Country[T.GER]    -0.0170      0.345     -0.049      0.961      -0.694       0.660
Country[T.IND]    -0.5484      0.368     -1.492      0.136      -1.269       0.172
Country[T.ME]      2.4776      1.245      1.990      0.047       0.037       4.918
Country[T.SA]      0.0868      0.271      0.320      0.749      -0.445       0.618
Country[T.SP]      0.1328      0.240      0.553      0.580      -0.337       0.603
Country[T.US]     -0.2149      0.372     -0.578      0.563      -0.943       0.513
==================================================================================

Chisq p-values:  [0.0, 0.0, 0.0]


# plotting
## merge in the original country codes provided in the dataset
countries = df[['Country', 'Country_code']].drop_duplicates().reset_index(drop=True)
df_cam2 = df_cam.merge(countries, how='left', on='Country_code')
df_cam2.head()

## bar graphs
g = sns.FacetGrid(df_cam2, col='Campaign', col_wrap=3)
g.map(sns.barplot, 'Country', 'Accepted (%)')
for ax, pval in zip(g.axes.flat, stat_results):
    ax.text(0, 65, "Chisq p-value: "+str(pval), fontsize=9) #add text;


df.head()


# calculate success rate (percent accepted)
cam_success = pd.DataFrame(df[['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Response']].mean()*100, 
                           columns=['Percent']).reset_index()


cam_success.head()


# plot
sns.barplot(x='Percent', y='index', data=cam_success.sort_values('Percent'), palette='Blues')
plt.xlabel('Accepted (%)')
plt.ylabel('Campaign')
plt.title('Marketing campaign success rate', size=16)

Text(0.5, 1.0, 'Marketing campaign success rate')


#Finding the average customer look like for the company:
# list of cols with binary responses
binary_cols = [col for col in df.columns if 'Accepted' in col] + ['Response', 'Complain']
binary_cols

['AcceptedCmp3',
 'AcceptedCmp4',
 'AcceptedCmp5',
 'AcceptedCmp1',
 'AcceptedCmp2',
 'Response',
 'Complain']


# list of cols for spending 
mnt_cols = [col for col in df.columns if 'Mnt' in col]
mnt_cols

['MntWines',
 'MntFruits',
 'MntMeatProducts',
 'MntFishProducts',
 'MntSweetProducts',
 'MntGoldProds',
 'TotalMnt']


# list of cols for channels
channel_cols = [col for col in df.columns if 'Num' in col] + ['TotalPurchases', 'TotalCampaignsAcc']
channel_cols

['NumDealsPurchases',
 'NumWebPurchases',
 'NumCatalogPurchases',
 'NumStorePurchases',
 'NumWebVisitsMonth',
 'TotalPurchases',
 'TotalCampaignsAcc']


# average customer demographics
demographics = pd.DataFrame(round(df.drop(columns=binary_cols+mnt_cols+channel_cols).mean(), 1), columns=['Average']).reindex([
    'Year_Birth', 'Year_Customer', 'Income', 'Dependents', 'Kidhome', 'Teenhome', 'Recency'])
demographics


#WHICH PRODUCT ARE PERFORMING THE BEST?

spending = pd.DataFrame(round(df[mnt_cols].mean(), 1), columns=['Average']).sort_values(by='Average').reset_index()
spending.head()


# plot
ax = sns.barplot(x='Average', y='index', data=spending, palette='Blues')
plt.ylabel('Amount spent on...')

## add text labels for each bar's value
for p,q in zip(ax.patches, spending['Average']):
    ax.text(x=q+40,
            y=p.get_y()+0.5,
            s=q,
            ha="center") ;


## add text labels for each bar's value
for p,q in zip(ax.patches, spending['Average']):
    ax.text(x=q+40,
            y=p.get_y()+0.5,
            s=q,
            ha="center") ;


channels = pd.DataFrame(round(df[channel_cols].mean(),1), columns = ['Average']).sort_values(by='Average').reset_index()


# plot
ax = sns.barplot(x='Average', y='index', data=channels, palette='Blues')
plt.ylabel('Number of...')

## add text labels for each bar's value
for p,q in zip(ax.patches, channels['Average']):
    ax.text(x=q+0.8,
            y=p.get_y()+0.5,
            s=q,
            ha="center") ;


Thank you!
I will add a PDF where I will showcase my summary of the whole process of Marketing Analytics

	ID	Dependents	Year_Customer	TotalMnt	TotalPurchases	TotalCampaignsAcc
0	1826	0	2014	4760	30	1
1	1	0	2014	2308	36	2
2	10476	1	2014	1004	22	0
3	1386	2	2014	44	8	0
4	5371	1	2014	364	16	2

Weight	Feature
11.5613 ± 0.3298	TotalPurchases
1.8977 ± 0.1119	NumCatalogPurchases
1.4006 ± 0.1682	NumWebPurchases
0.7977 ± 0.0609	NumDealsPurchases
0 ± 0.0000	Country_CA
… 43 more …

Weight	Feature
227.8780 ± 18.8462	TotalMnt
70.7519 ± 3.9459	MntWines
33.8252 ± 2.0694	MntMeatProducts
1.8676 ± 0.0755	MntGoldProds
1.1637 ± 0.0601	MntSweetProducts
1.0509 ± 0.0710	MntFruits
0 ± 0.0000	Country_IND
… 41 more …

Exploratory & Statistical Data Analysis on Marketing_analytics¶

Defining columns:¶

We have a total of 28 variables. I have segregated in the above list with common names. You can give a read to the above columns.¶

STATISTICAL ANALYSIS¶

Finding Significant relationship between Geographical Regional and Success of a Campaign:¶

Data Visualization¶

We conclude that:¶

We conclude that:¶

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp2	Response	Country
0	1826	1970	Graduation	Divorced	$84,835.00	0	0	6/16/14	189	104	379	111	189	218	1	4	4	6	1	0	0	1	SP
1	1	1961	Graduation	Single	$57,091.00	0	0	6/15/14	464	5	64	7	0	37	1	7	3	7	5	0	1	1	CA
2	10476	1958	Graduation	Married	$67,267.00	0	1	5/13/14	134	11	59	15	2	30	1	3	2	5	2	0	0	0	US
3	1386	1967	Graduation	Together	$32,474.00	1	1	5/11/14	10	0	1	0	0	0	1	1	0	2	7	0	0	0	AUS
4	5371	1989	Graduation	Single	$21,474.00	1	0	4/8/14	6	16	24	11	0	34	2	3	1	2	7	1	0	1	SP

	Education_Graduation	Marital_Status_Divorced	Marital_Status_Married	Marital_Status_Single	Marital_Status_Together	Country_AUS	Country_CA	Country_SP	Country_US	Year_Birth	Income	Kidhome	Teenhome	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp2	Response	Dependents	TotalMnt	Year_Customer	TotalPurchases	TotalCampaignsAcc
0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1970	51381.5	0	0	189	104	379	111	189	218	1	4	4	6	1	0	0	1	0	4760	2014	30	1
1	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1961	51381.5	0	0	464	5	64	7	0	37	1	7	3	7	5	0	1	1	0	2308	2014	36	2
2	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	1958	51381.5	0	1	134	11	59	15	2	30	1	3	2	5	2	0	0	0	1	1004	2014	22	0
3	1.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	1967	51381.5	1	1	10	0	1	0	0	0	1	1	0	2	7	0	0	0	2	44	2014	8	0
4	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1989	51381.5	1	0	6	16	24	11	0	34	2	3	1	2	7	1	0	1	1	364	2014	16	2

	Country_code	Campaign
0	ESP	AcceptedCmp1
1	CAN	AcceptedCmp1
2	USA	AcceptedCmp1
3	AUS	AcceptedCmp1
4	ESP	AcceptedCmp1

	index	Percent
0	AcceptedCmp1	6.437193
1	AcceptedCmp2	1.341082
2	AcceptedCmp3	7.286544
3	AcceptedCmp4	7.465355
4	AcceptedCmp5	7.241842

	Average
Year_Birth	1968.9
Year_Customer	2013.0
Income	51381.5
Dependents	1.0
Kidhome	0.4
Teenhome	0.5
Recency	49.1

	index	Average
0	MntFruits	26.3
1	MntSweetProducts	27.1
2	MntFishProducts	37.5
3	MntGoldProds	44.0
4	MntMeatProducts	166.9

Exploratory & Statistical Data Analysis on Marketing_analytics¶

I am just running my hands on this dataset to find out ideas about EDA on marketing domain and extracting insights and statistical analysis related to marketing topics.¶

Defining columns:¶

We have a total of 28 variables. I have segregated in the above list with common names. You can give a read to the above columns.¶

STATISTICAL ANALYSIS¶

Finding Significant relationship between Geographical Regional and Success of a Campaign:¶

Data Visualization¶

We conclude that:¶

We conclude that:¶